计算机工程与应用2025，Vol.61Issue(11)：238-248,11.DOI:10.3778/j.issn.1002-8331.2403-0252
多领域多模态融合网络的虚假新闻检测
Fake News Detection in Multi-Domain and Multi-Modal Fusion Networks
摘要
Abstract
The public is able to quickly obtain massive amounts of valuable information from the Internet,but it also facil-itates the widespread dissemination of fake news.Therefore,it becomes very important to find and mark out fake news on social media,and the fast and accurate identification of fake news can effectively prevent the formation of negative online public opinion and reduce the adverse social impact.On the basis of the existing fake news recognition research,a multi-domain and multi-modal fusion network(DMMFN)for fake news detection is constructed.In the DMMFN model,the BERT model is used to convert the text content of the fake news into text vectors,and the CLIP is used to extract the feature information of the images.By considering the correlation and interaction between text and images,a multimodal fusion network is established.Two combined matrices are formed to promote information interaction and fusion between different modalities.A multi-domain classification is introduced so that multi-modal features of different events can be mapped to the same feature space.The performance of this model is tested on Twitter and Weibo datasets,and the experimental results demonstrate that the DMMFN model outperforms baseline models such as SIMPLE and CCD in terms of accuracy,precision and F1 scores.关键词
虚假新闻/BERT/CLIP/多模态融合/多领域分类Key words
fake news/BERT/CLIP/multimodal fusion/multi-domain classification分类
信息技术与安全科学引用本文复制引用
焦世明,于凯..多领域多模态融合网络的虚假新闻检测[J].计算机工程与应用,2025,61(11):238-248,11.基金项目
新疆维吾尔自治区社会科学基金一般项目(21BTQ162) （21BTQ162）
新疆维吾尔自治区重点研发计划项目(2023B01032). （2023B01032）
